CommBank Analytics

> CommBank Analy-cs < Smart data driven marke-ng

> Short but sharp history

§  Datalicious was founded late 2007 §  Strong Omniture web analy-cs history §  Now 360 data agency with specialist team §  Combina-on of analysts and developers §  Carefully selected best of breed partners §  Driving industry best prac-ce (ADMA) §  Turning data into ac-onable insights §  Execu-ng smart data driven campaigns

April 2011 © Datalicious Pty Ltd 2

> Clients across all industries


> Wide range of data services


Data PlaAorms Data collec-on and processing Web analy-cs solu-ons Omniture, Google Analy-cs, etc Tag-‐less online data capture End-‐to-‐end data plaAorms IVR and call center repor-ng Single customer view

Insights Analy-cs Data mining and modelling Customised dashboards Tableau, SpoAire, SPSS, etc Media aMribu-on models Market and compe-tor trends Social media monitoring Customer profiling

Ac-on Campaigns Data usage and applica-on Marke-ng automa-on Alterian, SiteCore, Inxmail, etc Targe-ng and merchandising Internal search op-misa-on CRM strategy and execu-on Tes-ng programs

> Smart data driven marke-ng


Media AMribu-on

Op-mise channel mix

Tes-ng Improve usability

$$$

Targe-ng Increase relevance

Metric

s Framew

ork

Benchm

arking and

tren

ding

Metrics Fram

ework

Benchmarking and trending

> Workshop brief §  Defining a metrics framework – What to report on, when and why? – Matching strategic and tac-cal goals to metrics –  Covering all major categories of business goals

§  Finding and developing the right data –  Data sources across channels and goals – Meaningful trends vs. 100% accurate data –  Human and technological limita-ons

§  Campaign flow and media aZribu-on –  Designing a campaign flow including metrics – Media aZribu-on in a mul--‐channel environment


> Metrics framework

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


Awareness Interest Desire Ac-on Sa-sfac-on

> AIDA and AIDAS formulas


Social media

New media

Old media

> Importance of social media Search

WOM, blogs, reviews, ra-ngs, communi-es, social networks, photo sharing, video sharing

April 2011 © Datalicious Pty Ltd

Promo-on

9

Company Consumer

> Social as the new search


Reach (Awareness)

Engagement (Interest & Desire)

Conversion (Ac-on)

+Buzz (Sa-sfac-on)

> Simplified AIDAS funnel


People reached

People engaged

People converted

People delighted

> Marke-ng is about people


40% 10% 1%

People reached

People engaged

People converted

People delighted

> Addi-onal funnel breakdowns


40% 10% 1%

New prospects vs. exis-ng customers

Brand vs. direct response campaign


New vs. returning visitors


AU/NZ vs. rest of world


Prospect vs. customer

High vs. low value

Product affinity

Post code, age, sex, etc

Exercise: Funnel breakdowns


> Exercise: Funnel breakdowns §  List poten-ally insighaul funnel breakdowns –  Brand vs. direct response campaign – New prospects vs. exis-ng customers –  Baseline vs. incremental conversions –  Compe--ve ac-vity, i.e. none, a lot, etc –  Segments, i.e. age, loca-on, influence, etc –  Channels, i.e. search, display, social, etc –  Campaigns, i.e. this/last week, month, year, etc –  Products and brands, i.e. iphone, htc, etc – Offers, i.e. free minutes, free handset, etc – Devices, i.e. home, office, mobile, tablet, etc


> Geo-‐demographic segments


> Rela-ve or calculated metrics

§  Bounce rate §  Conversion rate §  Cost per acquisi-on §  Pages views per visit §  Product views per visit §  Cart abandonment rate §  Average order value


Exercise: Conversion metrics


> Exercise: Conversion metrics

§  Key conversion metrics differ by category – Commerce – Lead genera-on – Content publishing – Customer service


> Exercise: Conversion metrics


Source: Omniture Summit, MaZ Belkin, 2007

> Conversion funnel 1.0

April 2011

Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping informa-on, order confirma-on, etc

Conversion event

Campaign responses

© Datalicious Pty Ltd 24

> Conversion funnel 2.0

April 2011

Campaign responses (inbound spokes) Offline campaigns, banner ads, email marke-ng, referrals, organic search, paid search, internal promo-ons, etc

Landing page (hub)

Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registra-on, product comparison, product review, forward to friend, etc

© Datalicious Pty Ltd 25

> Addi-onal success metrics


Click Through

Add To Cart

Click Through

Page Bounce

Click Through $

Click Through

Call back request

Store Search ? $

$

$ Cart Checkout

Page Views

?

Product Views

> Conversion funnel design


Visits

Product Views

Cart Adds

Checkouts

Conversions

Visits

Non-‐Bounces*

Engagements**

Leads**

Conversions

* Non-‐bounce event ** Serialised events, i.e. once per visit

Exercise: Conversion funnel


> Exercise: Conversion funnel


Sen-ment

Reach Influence

> Measuring social media


Exercise: Metrics framework


Level Reach Engagement Conversion +Buzz

Level 1, people

Level 2, strategic

Level 3, tac-cal

Funnel breakdowns

> Exercise: Metrics framework


Level Reach Engagement Conversion +Buzz

Level 1 People

People reached

People engaged

People converted

People delighted

Level 2 Strategic

Display impressions ? ? ?

Level 3 Tac-cal

Interac-on rate, etc ? ? ?

Funnel Breakdowns Exis-ng customers vs. new prospects, products, etc

> Exercise: Metrics framework


€

IR −MIMI

= ROMI + BE

> ROI, ROMI, BE, etc

April 2011 © Datalicious Pty Ltd 34 €

IR −MIMI

= ROMI

€

R − II

= ROI R Revenue I Investment ROI Return on

investment IR Incremental

revenue MI Marke-ng

investment ROMI Return on

marke-ng investment

BE Brand equity

> Success: ROMI + BE

§  Establish incremental revenue (IR) –  Requires baseline revenue to calculate addi-onal revenue as well as revenue from cost savings

§  Establish marke-ng investment (MI) –  Requires all costs across technology, content, data and resources plus promo-ons and discounts

§  Establish brand equity contribu-on (BE) –  Requires addi-onal sok metrics to evaluate subscriber percep-ons, experience, altudes and word of mouth


€

IR −MIMI

= ROMI + BE

> Establishing a baseline


Switch all adver-sing off for a period of -me (unlikely) or establish a smaller control group that is representa-ve of the en-re popula-on (i.e. search term, geography, etc) and switch off selected channels one at a -me to minimise impact on overall conversions.

> Process is key to success



> Summary and ac-on items

§  Defining a metrics framework – Develop standardised metrics framework – Define addi-onal funnel breakdowns – Establish baseline and incremental – Define addi-onal success metrics – Define conversion funnels


> Recommended resources §  200501 WAA Key Metrics & KPIs §  200708 WAA Analy-cs Defini-ons Volume 1 §  200612 Omniture Effec-ve Measurement §  200804 Omniture Calculated Metrics White Paper §  200702 Omniture Effec-ve Segmenta-on Guide §  200810 Ronnestam Online Adver-sing And AIDAS §  201004 Al-meter Social Marke-ng Analy-cs §  201008 CSR Customer Sa-sfac-on Vs Delight §  Google “Enquiro Search Engine Results 2010 PDF” §  Google “Razorfish Ac-onable Analy-cs Report PDF” §  Google “Forrester Interac-ve Marke-ng Metrics PDF”


> Data sources

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


> Major data categories


Campaign data TV, print, call center, search, web analy-cs, ad serving, etc

Customer data Direct mail, call center, web analy-cs, emails, surveys, etc

Consumer data Geo-‐demographics, search, social, 3rd party research, etc

Compe-tor data Search, social, ad spend, 3rd party research, news, etc

Campaigns Customers

Compe-tors Consumers

> Digital data is plen-ful and cheap



People reached

People engaged

People converted

People delighted

> Mul-ple metrics data sources


Quan-ta-ve and qualita-ve research data

Website, call center and retail data

Social media data

Media and search data

Social media

TV/Print audience

Search audience

Banner audience

> Reach and channel overlap


> Es-ma-ng reach and overlap

§  Apply average unique visitor count per recorded unique user names to all unique visitor figures in Google Analy-cs, Omniture, etc.

§  Apply ra-o of total banner impressions to unique banner impressions from ad server to paid and organic search impressions in Google AdWords and Google Webmaster Tools.

§  Compare Google Keyword Tool impressions for a specific search term to reach for the same term in Google Ad Planner.

§  Or just add the reach figures for all channels up … April 2011 © Datalicious Pty Ltd 45

> Google data in Australia


Source: hZp://www.hitwise.com/au/resources/data-‐centre

> Search at all stages


Source: Inside the Mind of the Searcher, Enquiro 2004

> Search and brand strength


> Search and the product lifecycle


Nokia N-‐Series

Apple iPhone

> Search and media planning


> Search driving offline crea-ve


Exercise: Search insights


> Exercise: Search insights §  Iden-fy key category search terms –  Data from Google AdWords Keyword Tool –  Search for “google keyword tool” – Wordle and IBM Many Eyes for visualiza-ons –  Search for “wordle word clouds” and “ibm many eyes”

§  Iden-fy search term trends and compe-tors –  Google Trends and Google Search Insights –  Search for “google trends” and “google search insights”

§  Search and media planning –  DoubleClick Ad Planner by Google –  Search for “google ad planner”


> Cookie based tracking process


Source: Google Analy-cs, Jus-n Cutroni, 2007

What if: Someone deletes their cookies? Or uses a device that does not support JavaScript? Or uses two computers (work vs. home)? Or two people use the same computer?

The study examined data from two of the UK’s busiest ecommerce websites, ASDA and William Hill. Given that more than half of all page impressions on these sites are from logged-‐in users, they provided a robust sample to compare IP-‐based and cookie-‐based analysis against. The results were staggering, for example an IP-‐based approach overes-mated visitors by up to 7.6 -mes whilst a cookie-‐based approach overes-mated visitors by up to 2.3 -mes.

> Unique visitor overes-ma-on


Source: White Paper, RedEye, 2007

> Maximise iden-fica-on points

20%

40%

60%

80%

100%

120%

140%

160%

0 4 8 12 16 20 24 28 32 36 40 44 48

Weeks

−−− Probability of iden-fica-on through Cookies

April 2011 56 © Datalicious Pty Ltd

> Maximise iden-fica-on points


Mobile Home Work

Online Phone Branch

Campaign response data

> Combining data sources


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Duplica-on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email PlaAorm

Google Analy-cs

$

$

$

> Cookie expira-on impact


Banner Ad Click

Email Blast

Paid Search

Organic Search

Bid Mgmt

Ad Server

Email PlaAorm

Google Analy-cs

$

$

$

$

Expira-on

Banner Ad View

> CBA repor-ng plaAorms


Central Analy-cs PlaAorm

$

$

$

> De-‐duplica-on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$


De-‐duplica-on across channels

Exercise: Duplica-on impact


> Exercise: Duplica-on impact §  Double-‐coun-ng of conversions across channels can

have a significant impact on key metrics, especially CPA §  Example: Display ads and paid search

–  Total media budget of $10,000 of which 50% is spend on paid search and 50% on display ads

–  Total of 100 conversions across both channels with a channel overlap of 50%, i.e. both channels claim 100% of conversions based on their own repor-ng but once de-‐duplicated they each only contributed 50% of conversions

–  What are the ini-al CPA values and what is the true CPA? §  Solu-on: $50 ini-al CPA and $100 true CPA

–  $5,000 / 100 = $50 ini-al CPA and $5,000 / 50 = $100 true CPA (which represents a 100% increase)


> Single source of truth repor-ng


Insights Repor-ng




Google: “visualisa-on methods”

Exercise: Sta-s-cal significance


How many survey responses do you need if you have 10,000 customers?

How many email opens do you need to test 2 subject lines if your subscriber base is 50,000?

How many orders do you need to test 6 banner execu-ons if you serve 1,000,000 banners

Google “nss sample size calculator” April 2011 © Datalicious Pty Ltd 71

How many survey responses do you need if you have 10,000 customers?

369 for each ques-on or 369 complete responses

How many email opens do you need to test 2 subject lines if your subscriber base is 50,000? And email sends? 381 per subject line or 381 x 2 = 762 email opens

How many orders do you need to test 6 banner execu-ons if you serve 1,000,000 banners?

383 sales per banner execu-on or 383 x 6 = 2,298 sales

Google “nss sample size calculator” April 2011 © Datalicious Pty Ltd 72

> Addi-onal success metrics


Click Through

Add To Cart

Click Through

Page Bounce

Click Through $

Click Through

Call back request

Store Search ? $

$

$ Cart Checkout

Page Views

?

Product Views

Campaign response data



Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Behaviours plus transac-ons


one-‐off collec-on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira-on, etc predic-ve models based on data mining

propensity to buy, churn, etc historical data from previous transac-ons

average order value, points, etc

CRM Profile

Updated Occasionally

+ tracking of purchase funnel stage

browsing, checkout, etc tracking of content preferences

products, brands, features, etc tracking of external campaign responses

search terms, referrers, etc tracking of internal promo-on responses

emails, internal search, etc

Site Behaviour

Updated Con-nuously

Exercise: Customer IDs


> Exercise: Customer IDs


To reten-on messages To transac-onal data

From suspect to To customer

From behavioural data From awareness messages

Time Time prospect

> Sample customer level data


> Atomic labs tag-‐less analy-cs


§  Single point of data capture and processing

§  Real-‐-me queries to enrich website data

§ Mul-ple data export op-ons for web analy-cs

§  Enriching single-‐customer view website behaviour


Sen-ment analysis: People vs. machine


> Al-meter social analy-cs


Social Marke-ng Analy-cs is the discipline that helps companies measure, assess and explain the performance of social media ini-a-ves in the context of specific business objec-ves.

> Importance of calendar events


Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless

Calendar events to add context



§  Finding and developing the right data – Ensure de-‐duplica-on via central analy-cs – Check reports for sta-s-cal significance – Check data sources and their accuracy – Combine data sources across channels – Start popula-ng a calendar of events


> Recommended resources §  200311 UK RedEye Cookie Case Study §  200807 Kaushik Tracking Offline Conversion §  200904 Kaushik Standard Metrics Revisited §  201002 Kaushik 8 Compe--ve Intelligence Data Sources §  201005 Google Ad Planner Data Wrong By Up To 20% §  201005 MPI How Sta-s-cally Valid Is Your Survey §  201009 Google Analy-cs How To Tag Links §  200903 Coremetrics Conversion Benchmarks By Industry §  200906 WOM Online The People Vs Machines Debate §  201007 WSJ The Web's New Gold Mine Your Secrets §  201008 Adver-singAge Are Marketers Really Spying On You April 2011 © Datalicious Pty Ltd 86

> Media aMribu-on

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


Direct mail, email, etc

Facebook TwiMer, etc

> Campaign flow and calls to ac-on


POS kiosks, loyalty cards, etc

CRM program

Home pages, portals, etc

YouTube, blog, etc

Paid search

Organic search

Landing pages, offers, etc

PR, WOM, events, etc

TV, print, radio, etc

C2

C3

= Paid media

= Viral elements

Call center, retail stores, etc

= Coupons, surveys

Display ads, affiliates, etc

C1

Exercise: Campaign flow


TV/Print audience

Search audience

Banner audience

> Reach and channel overlap


Users are segmented before 1st ad is even served

> Ad server exposure test


Banner Impression $ TV/Print

Response Search

Response

Banner Impression $ Search

Response Direct

Response

Exposed group: 90% of users get branded message

Banner Impression $ Search

Response Direct

Response

Control group: 10% of users get non-‐branded message

> Indirect display impact







> Success aMribu-on models


Banner Ad $100

Email Blast

Paid Search $100

Banner Ad $100

Affiliate Referral $100

Success $100

Success $100

Banner Ad

Paid Search

Organic Search $100

Success $100

Last channel gets all credit

First channel gets all credit

All channels get equal credit

Print Ad $33

Social Media $33

Paid Search $33

Success $100

All channels get par-al credit

Paid Search

> First and last click aMribu-on


Chart shows percentage of channel touch points that lead to a conversion.

Neither first nor last-‐click measurement would provide true picture

Paid/Organic Search

Emails/Shopping Engines

> CBA first and last touch reports



Adobe campaign stack does not include organic channels or banner impressions and does not expire on any event, i.e. con-nues as long as the cookie is present.

Closer

SEM Generic

Banner View

TV Ad

> Full path to purchase


Influencer Influencer $

Banner Click Online

SEO Generic

Affiliate Click Offline

SEO Branded

Direct Visit

Email Update Abandon

Direct Visit

Social Media

SEO Branded

Introducer

> Search call to ac-on for offline



> PURLs boos-ng DM response rates


Text

> Poten-al calls to ac-on §  Unique click-‐through URLs §  Unique vanity domains or URLs §  Unique phone numbers §  Unique search terms §  Unique email addresses §  Unique personal URLs (PURLs) §  Unique SMS numbers, QR codes §  Unique promo-onal codes, vouchers §  Geographic loca-on (Facebook, FourSquare) §  Plus regression analysis of cause and effect


> Unique phone numbers

§  1 unique phone number –  Phone number is considered part of the brand – Media origin of calls cannot be established – Added value of website interac-on unknown

§  2-‐10 unique phone numbers – Different numbers for different media channels –  Exclusive number(s) reserved for website use –  Call origin data more granular but not perfect – Difficult to rotate and pause numbers


> Unique phone numbers §  10+ unique phone numbers – Different numbers for different media channels – Different numbers for different product categories – Different numbers for different conversion steps –  Call origin becoming useful to shape call script –  Feasible to pause numbers to improve integrity

§  100+ unique phone numbers – Different numbers for different website visitors –  Call origin and -me stamp enable individual match –  Call conversions matched back to search terms


> Jet Interac-ve phone call data


Closer

SEM Generic

Banner View

TV Ad

> Full path to purchase



Banner Click Online

SEO Generic

Affiliate Click Offline

SEO Branded

Direct Visit

Email Update Abandon

Direct Visit

Social Media

SEO Branded

Introducer

> Research online, shop offline


Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School

> Cross-‐channel impact


> Offline sales driven by online


Website research

Phone order

Retail order

Online order

Cookie

Adver-sing campaign

Credit check, fulfilment

Online order confirma-on

Virtual order confirma-on

Confirma-on email

Exercise: Offline conversions


> Exercise: Offline conversions

§  Email click-‐through aker purchase §  First online login aker purchase §  Unique website or visitor phone number §  Call back request or online chat §  Unique website promo-on code §  Unique printable vouchers §  Store locator searches § Make an appointment online


> Single source of truth repor-ng


Insights Repor-ng

> Where to collect the data


Referral visits Social media visits Organic search visits Paid search visits Email visits, etc

Web Analy-cs Banner impressions

Banner clicks +

Paid search clicks

Ad Server

Lacking ad impressions Less granular & complex

Lacking organic visits More granular & complex

> Raw aMribu-on data

Web Analy-cs AFFILIATE > SEO > $$$ SEM > SOCIAL > EMAIL > DIRECT > $$$

Ad Server 01/01/2011 12:00 AD IMPRESSION 01/01/2011 12:05 SEO 07/01/2011 17:00 EMAIL 08/01/2011 15:00 $$$


> Combine purchase paths


Mobile Home Work

Tablet Media Etc



> Understanding channel mix



> Website entry survey


Channel % of Conversions

Straight to Site 27%

SEO Branded 15%

SEM Branded 9%

SEO Generic 7%

SEM Generic 14%

Display Adver-sing 7%

Affiliate Marke-ng 9%

Referrals 5%

Email Marke-ng 7%

De-‐duped Campaign Report

} Channel % of Influence

Word of Mouth 32%

Blogging & Social Media 24%

Newspaper Adver-sing 9%

Display Adver-sing 14%

Email Marke-ng 7%

Retail Promo-ons 14%

Greatest Influencer on Branded Search / STS

Conversions aZributed to search terms that contain brand keywords and direct website visits are most likely not the origina-ng channel that generated the awareness and as such conversion credits should be re-‐allocated.

> Adjus-ng for offline impact


+15 +5 +10 -‐15 -‐5 -‐10



> ClearSaleing media aMribu-on


Closer

25%

> Success aMribu-on models



25% Even AMrib.

Exclusion AMrib.

PaMern AMrib.

25% 25%

Introducer

33% 33% 33% 0%

30% 20% 20% 30%

Closer

Channel 1

Channel 1

Channel 1

> Path across different segments



Channel 2

Channel 2 Channel 3

Channel 2 Channel 3 Product 4

Channel 3

Channel 4

Channel 4

Introducer

Product A vs. B

New prospects

Exis-ng customers

Exercise: AMribu-on model


Closer

25%

> Exercise: AMribu-on models



25% Even AMrib.

Exclusion AMrib.

Custom AMrib.

25% 25%

Introducer

33% 33% 33% 0%

? ? ? ?

> Common aMribu-on models

§  Allocate more conversion credits to more recent touch points for brands with a strong baseline to s-mulate repeat purchases

§  Allocate more conversion credits to more recent touch points for brands with a direct response focus

§  Allocate more conversion credits to ini-a-ng touch points for new and expensive brands and products to insert them into the mindset


> Media aMribu-on phases §  Phase 1: De-‐duplica-on –  Conversion de-‐duplica-on across all channels –  Requires one central repor-ng plaaorm –  Limited to first/last click aZribu-on

§  Phase 2: Direct response pathing –  Response pathing across paid and organic channels –  Only covers clicks and not mere banner views –  Can be enabled in Google Analy-cs and Omniture

§  Phase 3: Full purchase path –  Direct response tracking including banner exposure –  Google Analy-cs and Omniture data collec-on limited –  Easier to import addi-onal channels into ad server



§  Campaign flow and media aZribu-on – Draw campaign flow for your company – Check plaaorm cookie expira-on periods – Enable pathing of direct campaign responses –  Inves-gate addi-onal pathing op-ons –  Inves-gate how to track offline conversions


> Recommended resources §  200812 ComScore How Online Adver-sing Works §  200905 iProspect Research Study Search And Display §  200904 ClearSaleing American AZribu-on Index §  201003 Datalicious Tying Offline Sales To Online Media §  Google: “Forrester Campaign AZribu-on Framework PDF”



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Data > Insights > Ac-on

Date post:	01-Nov-2014
Category:	Technology
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